


'''simple docstring'''


def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				if collection == []:
								return []

				# get some information about the collection
				_SCREAMING_SNAKE_CASE					:			Any							= len(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			int							= max(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Dict							= min(SCREAMING_SNAKE_CASE__	)

				# create the counting array
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= coll_max + 1 - coll_min
				_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= [0] * counting_arr_length

				# count how much a number appears in the collection
				for number in collection:
								counting_arr[number - coll_min] += 1

				# sum each position with it's predecessors. now, counting_arr[i] tells
				# us how many elements <= i has in the collection
				for i in range(1				, SCREAMING_SNAKE_CASE__	):
								_SCREAMING_SNAKE_CASE					:			List[Any]							= counting_arr[i] + counting_arr[i - 1]

				# create the output collection
				_SCREAMING_SNAKE_CASE					:			Tuple							= [0] * coll_len

				# place the elements in the output, respecting the original order (stable
				# sort) from end to begin, updating counting_arr
				for i in reversed(range(0				, SCREAMING_SNAKE_CASE__	)	):
								_SCREAMING_SNAKE_CASE					:			Any							= collection[i]
								counting_arr[collection[i] - coll_min] -= 1

				return ordered







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				return "".join([chr(SCREAMING_SNAKE_CASE__	) for i in counting_sort([ord(SCREAMING_SNAKE_CASE__	) for c in string]	)]	)


if __name__ == "__main__":
				# Test string sort
				assert counting_sort_string('thisisthestring') == "eghhiiinrsssttt"

				UpperCAmelCase_						:  Union[str, Any]				  =						input('Enter numbers separated by a comma:\n').strip()
				UpperCAmelCase_						:  List[str]				  =						[int(item) for item in user_input.split(',')]
				print(counting_sort(unsorted))








'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class        lowercase__    (		_snake_case	,	unittest.TestCase   ):





							'''simple docstring'''


							A_				: Dict       =    KandinskyVaaPipeline
							A_				: List[str]       =    [
							    """image_embeds""",
							    """negative_image_embeds""",
							]
							A_				: Optional[int]       =    ["""image_embeds""", """negative_image_embeds"""]
							A_				: Optional[Any]       =    [
							    """generator""",
							    """height""",
							    """width""",
							    """latents""",
							    """guidance_scale""",
							    """num_inference_steps""",
							    """return_dict""",
							    """guidance_scale""",
							    """num_images_per_prompt""",
							    """output_type""",
							    """return_dict""",
							]
							A_				: Dict       =    False






							@property
							def UpperCAmelCase_      (       self    ):
											return 32






							@property
							def UpperCAmelCase_      (       self    ):
											return 32






							@property
							def UpperCAmelCase_      (       self    ):
											return self.time_input_dim






							@property
							def UpperCAmelCase_      (       self    ):
											return self.time_input_dim * 4






							@property
							def UpperCAmelCase_      (       self    ):
											return 100






							@property
							def UpperCAmelCase_      (       self    ):
											torch.manual_seed(0    )

											_SCREAMING_SNAKE_CASE					:			Tuple							= {
											    """in_channels""": 4,
											    # Out channels is double in channels because predicts mean and variance
											    """out_channels""": 8,
											    """addition_embed_type""": """image""",
											    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
											    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
											    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
											    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
											    """layers_per_block""": 1,
											    """encoder_hid_dim""": self.text_embedder_hidden_size,
											    """encoder_hid_dim_type""": """image_proj""",
											    """cross_attention_dim""": self.cross_attention_dim,
											    """attention_head_dim""": 4,
											    """resnet_time_scale_shift""": """scale_shift""",
											    """class_embed_type""": None,
											}

											_SCREAMING_SNAKE_CASE					:			Any							= UNetaDConditionModel(**__snake_case    )
											return model






							@property
							def UpperCAmelCase_      (       self    ):
											return {
											    "block_out_channels": [32, 64],
											    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
											    "in_channels": 3,
											    "latent_channels": 4,
											    "layers_per_block": 1,
											    "norm_num_groups": 8,
											    "norm_type": "spatial",
											    "num_vq_embeddings": 12,
											    "out_channels": 3,
											    "up_block_types": [
											        "AttnUpDecoderBlock2D",
											        "UpDecoderBlock2D",
											    ],
											    "vq_embed_dim": 4,
											}






							@property
							def UpperCAmelCase_      (       self    ):
											torch.manual_seed(0    )
											_SCREAMING_SNAKE_CASE					:			Tuple							= VQModel(**self.dummy_movq_kwargs    )
											return model






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			int							= self.dummy_unet
											_SCREAMING_SNAKE_CASE					:			List[str]							= self.dummy_movq

											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= DDIMScheduler(
											    num_train_timesteps=1000       ,   beta_schedule="""linear"""       ,   beta_start=0.00085       ,   beta_end=0.012       ,   clip_sample=__snake_case       ,   set_alpha_to_one=__snake_case       ,   steps_offset=1       ,   prediction_type="""epsilon"""       ,   thresholding=__snake_case       ,   )

											_SCREAMING_SNAKE_CASE					:			Dict							= {
											    """unet""": unet,
											    """scheduler""": scheduler,
											    """movq""": movq,
											}
											return components






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case=0    ):
											_SCREAMING_SNAKE_CASE					:			int							= floats_tensor((1, self.text_embedder_hidden_size)       ,   rng=random.Random(__snake_case    )    ).to(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			List[str]							= floats_tensor((1, self.text_embedder_hidden_size)       ,   rng=random.Random(seed + 1    )    ).to(
											    __snake_case    )
											if str(__snake_case    ).startswith("""mps"""    ):
															_SCREAMING_SNAKE_CASE					:			str							= torch.manual_seed(__snake_case    )
											else:
															_SCREAMING_SNAKE_CASE					:			Dict							= torch.Generator(device=__snake_case    ).manual_seed(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Tuple							= {
											    """image_embeds""": image_embeds,
											    """negative_image_embeds""": negative_image_embeds,
											    """generator""": generator,
											    """height""": 64,
											    """width""": 64,
											    """guidance_scale""": 4.0,
											    """num_inference_steps""": 2,
											    """output_type""": """np""",
											}
											return inputs






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= """cpu"""

											_SCREAMING_SNAKE_CASE					:			Dict							= self.get_dummy_components()

											_SCREAMING_SNAKE_CASE					:			List[Any]							= self.pipeline_class(**__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= pipe.to(__snake_case    )

											pipe.set_progress_bar_config(disable=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Any							= pipe(**self.get_dummy_inputs(__snake_case    )    )
											_SCREAMING_SNAKE_CASE					:			Tuple							= output.images

											_SCREAMING_SNAKE_CASE					:			Dict							= pipe(
											    **self.get_dummy_inputs(__snake_case    )       ,   return_dict=__snake_case       ,   )[0]

											_SCREAMING_SNAKE_CASE					:			Tuple							= image[0, -3:, -3:, -1]
											_SCREAMING_SNAKE_CASE					:			List[Any]							= image_from_tuple[0, -3:, -3:, -1]

											assert image.shape == (1, 64, 64, 3)

											_SCREAMING_SNAKE_CASE					:			Dict							= np.array(
											    [0.6237976, 1.0, 0.36441332, 1.0, 0.70639634, 0.29877186, 0.85652125, 0.5216843, 0.54454046]    )

											assert (
											    np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
											), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""

											assert (
											    np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1e-2
											), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""





@slow
@require_torch_gpu
class        lowercase__    (		unittest.TestCase   ):





							'''simple docstring'''


							def UpperCAmelCase_      (       self    ):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			List[str]							= load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
											    """/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"""    )

											_SCREAMING_SNAKE_CASE					:			List[Any]							= KandinskyVaaPriorPipeline.from_pretrained(
											    """kandinsky-community/kandinsky-2-2-prior"""       ,   torch_dtype=torch.floataa    )
											pipe_prior.to(__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Tuple							= KandinskyVaaPipeline.from_pretrained(
											    """kandinsky-community/kandinsky-2-2-decoder"""       ,   torch_dtype=torch.floataa    )
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= pipeline.to(__snake_case    )
											pipeline.set_progress_bar_config(disable=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Dict							= """red cat, 4k photo"""

											_SCREAMING_SNAKE_CASE					:			List[str]							= torch.Generator(device="""cuda"""    ).manual_seed(0    )
											_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[str]							= pipe_prior(
											    __snake_case       ,   generator=__snake_case       ,   num_inference_steps=5       ,   negative_prompt=""""""       ,   ).to_tuple()

											_SCREAMING_SNAKE_CASE					:			Dict							= torch.Generator(device="""cuda"""    ).manual_seed(0    )
											_SCREAMING_SNAKE_CASE					:			List[str]							= pipeline(
											    image_embeds=__snake_case       ,   negative_image_embeds=__snake_case       ,   generator=__snake_case       ,   num_inference_steps=100       ,   output_type="""np"""       ,   )

											_SCREAMING_SNAKE_CASE					:			List[Any]							= output.images[0]

											assert image.shape == (512, 512, 3)

											assert_mean_pixel_difference(__snake_case       ,   __snake_case    )




